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Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study

Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study

The introduction of large language models has disrupted this field, with many papers describing their potential value in clinical note generation and multiple companies now offering digital scribe systems [13-15]. However, an evaluation on the potential impact of such a system on documentation time, including the assessment of quality and user experiences is not available to date.

Marieke Meija van Buchem, Ilse M J Kant, Liza King, Jacqueline Kazmaier, Ewout W Steyerberg, Martijn P Bauer

JMIR AI 2024;3:e60020

Viability of Open Large Language Models for Clinical Documentation in German Health Care: Real-World Model Evaluation Study

Viability of Open Large Language Models for Clinical Documentation in German Health Care: Real-World Model Evaluation Study

The use of fixed model versions, as offered by some providers, is not practicable in the long term, as older models are often removed after the release of updates; in the case of Open AI, after 3 months [5]. An alternative is the use of nonproprietary AI models. In these models, the architecture as well as the trained parameters are available to the user.

Felix Heilmeyer, Daniel Böhringer, Thomas Reinhard, Sebastian Arens, Lisa Lyssenko, Christian Haverkamp

JMIR Med Inform 2024;12:e59617

An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study

An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study

Different models, such as basic, statistical, machine learning (support vector regression [SVR], random forest, gradient boosting, etc), and deep learning (long short-term memory and gated recurrent unit) models, were compared to identify the best opportunity for obtaining high-precision predictions. There are different learning models for regression and classification. Given the nature of the data sets and the continuous nature of the variables to be predicted, a regression learning model was used.

Ayman Hassan, Rachid Benlamri, Trina Diner, Keli Cristofaro, Lucas Dillistone, Hajar Khallouki, Mahvareh Ahghari, Shalyn Littlefield, Rabail Siddiqui, Russell MacDonald, David W Savage

JMIR Form Res 2024;8:e54009

Considering the Role of Human Empathy in AI-Driven Therapy

Considering the Role of Human Empathy in AI-Driven Therapy

Recent breakthroughs in artificial intelligence (AI) language models have elevated this vision, as evidenced by a growing body of literature indicating varying degrees of efficacy. For instance, studies demonstrate that digital chatbots are proficient in delivering psychoeducation and improving treatment adherence over short durations [3].

Matan Rubin, Hadar Arnon, Jonathan D Huppert, Anat Perry

JMIR Ment Health 2024;11:e56529

An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study

An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study

Finally, we propose a prompt engineering framework to build and deploy zero-shot NLP models for the clinical domain. This study covers 3 state-of-the-art LMs, including GPT-3.5, Gemini, and LLa MA-2, to assess the generalizability of the findings across various models. This work yields novel insights and guidelines for prompt engineering specifically for clinical NLP tasks.

Sonish Sivarajkumar, Mark Kelley, Alyssa Samolyk-Mazzanti, Shyam Visweswaran, Yanshan Wang

JMIR Med Inform 2024;12:e55318

Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study

Identifying Predictive Risk Factors for Future Cognitive Impairment Among Chinese Older Adults: Longitudinal Prediction Study

In addition to known risk factors, it is unclear if existing prediction models for future cognitive impairment are equally accurate across different socioeconomic groups in China. Several published models have reported areas under the receiver operating characteristic curve (AUCs) greater than 0.80 in their development cohorts [5,7,8], but each model has only been tested on the general population. Nearly all existing models make predictions by leveraging measures of cognition, age, and education.

Collin Sakal, Tingyou Li, Juan Li, Xinyue Li

JMIR Aging 2024;7:e53240

Technology-Supported Guidance Models to Stimulate Nursing Students’ Self-Efficacy in Clinical Practice: Scoping Review

Technology-Supported Guidance Models to Stimulate Nursing Students’ Self-Efficacy in Clinical Practice: Scoping Review

Traditionally, clinical practice has played a crucial role in nursing education, organized by guidance models. These models consist of procedures, meetings, and collaboration, aiming to facilitate the development of nursing students’ competencies in clinical practice through cooperation between health care and educational institutions [21].

Paula Bresolin, Simen A Steindal, Hanne Maria Bingen, Jaroslav Zlamal, Jussara Gue Martini, Eline Kaupang Petersen, Andréa Aparecida Gonçalves Nes

JMIR Nursing 2024;7:e54443

Mapping Theories, Models, and Frameworks to Evaluate Digital Health Interventions: Scoping Review

Mapping Theories, Models, and Frameworks to Evaluate Digital Health Interventions: Scoping Review

One way of facilitating the systematic understanding and explanation of the complex interactions between users, practices, technology, and health system factors that underpin research questions [20,21] is to use theories, models, and frameworks (TMFs). There is a wide range of TMFs that have been used in studies of knowledge translation [22,23], and implementation science [24] (examples of more than 40 TMFs are cited).

Geneviève Rouleau, Kelly Wu, Karishini Ramamoorthi, Cherish Boxall, Rebecca H Liu, Shelagh Maloney, Jennifer Zelmer, Ted Scott, Darren Larsen, Harindra C Wijeysundera, Daniela Ziegler, Sacha Bhatia, Vanessa Kishimoto, Carolyn Steele Gray, Laura Desveaux

J Med Internet Res 2024;26:e51098